### 3.1 Quality Grade The dataset earns an **A (Excellent)**, with completeness at 95% and referential integrity at 100%. For business decisions, this means the tennis serve, return, and raw match data can support analysis with reasonable confidence. The remaining ~5% of missing values is the main caveat, and its location matters more than its size. The headline reports a 97% score while the shared scorecard shows 95% overall, so confirm which figure is authoritative before quoting it externally. ### 3.2 Key Risk Areas | Risk | Severity | Affected Tables | |---|---|---| | No significant risks detected | None | All 5 tables | No risks were flagged, but two points deserve attention. Uniqueness is not scored (N/A), and no validated joins were detected. The 100% integrity score therefore reflects an absence of detected breaks, not proven links between tables. Before player-level reporting or modeling, confirm how `players_man_` (462 rows) and `players_tournament_man_` (6,422 rows) connect to the three match-level tables. ### 3.3 Remediation Priorities - **Locate the ~5% completeness gap.** Run a column-level review of `raw_kaggle`, `return_kaggle`, and `serve_kaggle`, the three largest tables, to see whether the gaps sit in key performance metrics or in minor descriptive fields. This determines whether the gaps affect analysis. - **Reconcile row counts across the match tables.** `raw_kaggle` has 237,205 rows, `return_kaggle` 237,196, and `serve_kaggle` 237,185. The differences of 9 to 20 rows are small. If these tables describe the same matches, find out which records are missing and why, so serve and return statistics are not compared on different populations. - **Define and test the player and tournament links.** No validated joins exist. Document how `players_man_` and `players_tournament_man_` relate to the match tables, then test those links so the integrity score reflects real relationships. - **Establish a uniqueness check.** Agree on what identifies a single record in each table (for example, one player or one tournament entry), then monitor for duplicates, especially in the 237,000-row tables where duplication would quietly skew averages. - **Set a quality baseline.** Record the current 95% completeness figure as the benchmark for future data refreshes, so any decline is caught before it reaches reports.